In this session, we will use Black Friday Data in Kaggle to study how to make the following graphical displays.
Here is a list of common arguments: - col: a vector of colors - main: title for the plot - xlim or ylim: limits for the x or y axis - xlab or ylab: a label for the x or y axis - font: font used for text, 1= plain, 2= bold, 3= italic, 4= bold italic - font.axis: font used for axis - cex.axis: font size for x and y axis - font.lab: font for x and y labels - cex.lba: font size for x and y labels
In this session, we will use Black Friday Data in Kaggle to study how to make the following graphical displays.
Here is a list of common arguments: - col: a vector of colors - main: title for the plot - xlim or ylim: limits for the x or y axis - xlab or ylab: a label for the x or y axis - font: font used for text, 1= plain, 2= bold, 3= italic, 4= bold italic - font.axis: font used for axis - cex.axis: font size for x and y axis - font.lab: font for x and y labels - cex.lba: font size for x and y labels
In order to understand the customer purchases behavior against carious products of different categories, the retail company “ABC Private Limited”, in United Kingdom, shared purchase summary of various customers for selected high volume products from last month. the data contains the following variables.
Rows: 550,068
Columns: 12
$ User_ID <dbl> 1000001, 1000001, 1000001, 1000001, 1000002…
$ Product_ID <chr> "P00069042", "P00248942", "P00087842", "P00…
$ Gender <chr> "F", "F", "F", "F", "M", "M", "M", "M", "M"…
$ Age <chr> "0-17", "0-17", "0-17", "0-17", "55+", "26-…
$ Occupation <dbl> 10, 10, 10, 10, 16, 15, 7, 7, 7, 20, 20, 20…
$ City_Category <chr> "A", "A", "A", "A", "C", "A", "B", "B", "B"…
$ Stay_In_Current_City_Years <chr> "2", "2", "2", "2", "4+", "3", "2", "2", "2…
$ Marital_Status <dbl> 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0…
$ Product_Category_1 <dbl> 3, 1, 12, 12, 8, 1, 1, 1, 1, 8, 5, 8, 8, 1,…
$ Product_Category_2 <dbl> NA, 6, NA, 14, NA, 2, 8, 15, 16, NA, 11, NA…
$ Product_Category_3 <dbl> NA, 14, NA, NA, NA, NA, 17, NA, NA, NA, NA,…
$ Purchase <dbl> 8370, 15200, 1422, 1057, 7969, 15227, 19215…
Bar Chart is a graphical display good for the general audience. Here, we study the distribution of Age Group of the compnay’s customers who purchased their products on Black Friday. Usage:barplot(height, …)
A bar chart can be horizontal or vertical. Using the argument col, we can assign a color for bars. The argument main could be used to change the title of the figure. We can use RGB color code to assign colors.
Note: The margin of a figure could be set using the c(bottom, left, top, right).
Similarly, We can use pie chart to study the distribution of the city category.
Usage: pie(height,..)
Tip: Use color palette to chose colors (Google search: color scheme generator).
Histogram is used when we want to study the distribution of a qunatitative variable. Here we study the distribution of customer purchase amount.
Usage: hist(x, …)
In general, a boxplot is used when we want to compare the distributions of several quantitative variables. In the follwoing we study the distribution of customer purchase amount among different age groups.
When we wanta to study the relationship of two quantitative variables, a scatterplot can be used. since this data set doesn’t have another quantitative variable, we will use the built-in data mtcars in R. Then we study the relationship of miles per gallon against the weight of vehicles.
Since the Black Friday Data is not time series data, it is not appropriate to use a line plot. In the following code chunk, we create a data frame using the forecasted highest temperatures from July 13 to July 22 in 2022(The Weather Chanel).